Search Results for author: Rousslan Fernand Julien Dossa

Found 3 papers, 3 papers with code

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning

1 code implementation • 5 Feb 2024 • Shengyi Huang, Quentin Gallouédec, Florian Felten, Antonin Raffin, Rousslan Fernand Julien Dossa, Yanxiao Zhao, Ryan Sullivan, Viktor Makoviychuk, Denys Makoviichuk, Mohamad H. Danesh, Cyril Roumégous, Jiayi Weng, Chufan Chen, Md Masudur Rahman, João G. M. Araújo, Guorui Quan, Daniel Tan, Timo Klein, Rujikorn Charakorn, Mark Towers, Yann Berthelot, Kinal Mehta, Dipam Chakraborty, Arjun KG, Valentin Charraut, Chang Ye, Zichen Liu, Lucas N. Alegre, Alexander Nikulin, Xiao Hu, Tianlin Liu, Jongwook Choi, Brent Yi

As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.

reinforcement-learning Reinforcement Learning (RL)

163

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Code

A2C is a special case of PPO

1 code implementation • 18 May 2022 • Shengyi Huang, Anssi Kanervisto, Antonin Raffin, Weixun Wang, Santiago Ontañón, Rousslan Fernand Julien Dossa

Advantage Actor-critic (A2C) and Proximal Policy Optimization (PPO) are popular deep reinforcement learning algorithms used for game AI in recent years.

reinforcement-learning Reinforcement Learning (RL)

Paper
Code

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms

2 code implementations • 16 Nov 2021 • Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga

CleanRL is an open-source library that provides high-quality single-file implementations of Deep Reinforcement Learning algorithms.

Benchmarking reinforcement-learning +2

4,463

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Code

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